Abstract: Explicit finite difference method has been widely used for seismic modeling in heterogeneous media with strong discontinuities in physical properties. In such cases, due to stability considerations, the time step size is primarily determined by the medium with higher wave speed propagation, resulting that the higher the speed, the lower the time step needs to be to ensure stability throughout the whole domain. Therefore, the use of different temporal discretizations can greatly reduce the computational cost involved when solving this kind of problem. In this paper we propose an algorithm for the local temporal discretization setting named Region Triangular Transition (RTT), which allows the local temporal discretizations to be related by any integer value that enables these discretizations to operate at the stability limit of the finite difference approximations used.

Abstract: This study uses Differential Evolution to identify the coefficients of second-order differential equations of self-excited vibrations from a time signal. The motivation is found in the ample occurrence of this vibration type in engineering and physics, in particular in the real-life problem of vibrations of hydraulic structure gates. In the proposed method, an equation structure is assumed at the level of the ordinary differential equation and a population of candidate coefficient vectors undergoes evolutionary training. In this way the numerical constants of non-linear terms of various self-excited vibration types were recovered from the time signal and the velocity value only at the initial time. Comparisons are given regarding accuracy and computing time. The presented evolutionary method shows good promise for future application in engineering systems, in particular operational early-warning systems that recognise oscillations with negative damping before they can cause damage.

Abstract: Interactive photo-realistic representation of dynamic liquid volumes is a challenging task for today's GPUs and state-of-the-art visualization algorithms. Methods of the last two decades consider either static volumetric datasets applying several optimizations for volume casting, or dynamic volumetric datasets with rough approximations to realistic rendering. Nevertheless, accurate real-time visualization of dynamic datasets is crucial in areas of scientific visualization as well as areas demanding for accurate rendering of feature-rich datasets. An accurate and thus realistic visualization of such datasets leads to new challenges: due to restrictions given by computational performance, the datasets may be relatively small compared to the screen resolution, and thus each voxel has to be rendered highly oversampled. With our volumetric datasets based on a real-time lattice Boltzmann fluid simulation creating dynamic cavities and small droplets, existing real-time implementations are not applicable for a realistic surface extraction. This work presents a volume tracing algorithm capable of producing multiple refractions which is also robust to small droplets and cavities. Furthermore we show advantages of our volume tracing algorithm compared to other implementations.

Abstract: Continuous Interactive Simulation (CIS) maps computational problems concerning the control of dynamical systems to physical tasks in a 3D virtual environment for users to perform. However, deciding on the best mapping for a particular problem is not straightforward. This paper considers how a motor learning perspective can assist when designing such mappings. To examine this issue an experiment was performed to compare an arbitrary mapping with one designed by considering a range of motor learning factors. The particular problem studied was a nonlinear policy setting problem from economics. The results show that choices about how a problem is presented can indeed have a large effect on the ability of users to solve the problem. As a result we recommend the development of guidelines for the application of CIS based on motor learning considerations.

Abstract: Mining dynamic and non-trivial patterns of interactions of functional brain network has gained significance due to the recent advances in the field of computational neuroscience. Sophisticated data search capabilities, advanced signal processing techniques, statistical methods, complex network and graph mining algorithms to unfold and discover hidden patterns in the functional brain network supported with efficient visualization techniques are essential for making potential inferences of the results obtained. Visualization of change in activity during cognitive function is useful to discover and get insights into the hidden, novel and complex neuronal patterns and trends during the normal and cognitive load conditions from the graph/temporal representation of the functional brain network. This paper explores novel methods to explore and model the dynamics and complexity of the brain. It also uses a new tool called Functional Brain Network Analysis and Visualization (FBNAV) tool to visualize the outcomes of various computational analyses to enable us to identify and study the changing neuronal patterns during various states of the brain activity using augmented/customised Topoplots and Headplots. These techniques may be helpful to locate and identify patterns in certain abnormal mental states resulting due to some mental disorders such as stress.